Abstract
This Paper Analyzes the Impact of Different Detrending Approaches on the Performance of a Variety of Computational Intelligence (Ci) Models. Three Approaches Are Compared: Linear, Nonlinear Detrending (based on Empirical Mode Decomposition) and First-Differencing. Five Representative Ci Methods Are Evaluated: Dynamic Evolving Neural-Fuzzy Inference System (DENFIS), Gaussian Process (GP), Multilayer Perceptron (MP), Optimally Pruned Extreme Learning Machine (Op-Elm) and Support Vector Machines (SVM). Four Major Conclusions Are Drawn from Experiments Performed on Six Time Series Benchmarks: 1) Qualitatively, the Effect of Detrending is Remarkably Uniform for All the Ci Methods Considered, 2) Extraction of the overall Trend Does Not Improve Performance in General 3) the EMD-Based Method Provides Better Performance Than Linear Detrending (While the Difference is Negligible in Most Cases, It is Noticeable in Some Cases), and 4) First-Differencing, While Effective in Some Cases, Can Be Counterproductive for Series Showing Common Patterns. © 2010 IEEE.
Recommended Citation
F. Montesino Pouzols and A. Lendasse, "Effect of Different Detrending Approaches on Computational Intelligence Models of Time Series," Proceedings of the International Joint Conference on Neural Networks, article no. 5596314, Institute of Electrical and Electronics Engineers, Jan 2010.
The definitive version is available at https://doi.org/10.1109/IJCNN.2010.5596314
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-142446917-8
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2010
